In this section, we show how approval strategies can be defined as a sequence of penalized empirical risk minimization (ERM) problems.
The objective function in these ERM problems includes two regularization terms: one expresses our optimism in modifications proposed by the model developer, and the other expresses our optimism in our ability to predict future performance using historical data.
By varying the hyperparameters that scale these two regularization terms, we recover a wide range of strategies that includes or approximates previously proposed strategies.
Our formulation is inspired by the optimistic mirror descent algorithm [1]} but considers a much wider range of “optimism.”
